Lex Fridman Podcast - Kevin Scott: Microsoft CTO

Episode Date: August 1, 2019

Kevin Scott is the CTO of Microsoft. Before that, he was the Senior Vice President of Engineering and Operations at LinkedIn. And before that, he oversaw mobile ads engineering at Google. This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on iTunes or support it on Patreon.

The following is a conversation with Kevin Scott, the CTO of Microsoft. Before that, he was the senior vice president of engineering and operations at LinkedIn, and before that, he oversaw mobile ads engineering at Google. He also has a podcast called Behind the Tech with Kevin Scott, which I'm a fan of. This was a fun and wide-ranging conversation that covered many aspects of computing. It happened over a month ago before the announcement of Microsoft's investment open AI that a few people have asked me about. I'm sure there'll be one or two people in the future that'll talk with me about the impact of that investment. This is the Artificial Intelligence Podcast.

If you enjoy it, subscribe on YouTube, give it 5 stars and iTunes, support it on Patreon, or simply connect with me on Twitter, at Lex Friedman, spelled F-R-I-D-M-A-N. And I'd like to give a special thank you to Tom and Nalanti Bighausen, for their support of the podcast on Patreon. Thanks Tom and Nalanti, hope I didn't mess up your last name too bad. Your support means a lot and inspires me to keep the series going. And now here's my conversation with Kevin Scott. You've described yourself as a kid in a candy store at Microsoft because of all the interesting projects that are going on. Can you try to do the impossible task and give a brief whirlwind view of all the

spaces that Microsoft is working in? Both research and product. If you include research, it becomes So, I think broadly speaking, Microsoft's product portfolio includes everything from, you know, big cloud business, like a big set of SaaS services. We have, you know, sort of the original, or like some of what are among the original productivity software products that everybody uses. We have an operating system business. We have a hardware business where we make everything from computer mice and headphones to high-end,

high-impersonal computers and laptops. We have a fairly broad-ranging research group where we have people doing everything from economics research. So, this is really, really smart, young economist, Glenn Wile, who, like my group, works with a lot who's doing this research on these things called Radical Markets. He's written an entire technical book about this whole notion of Radical Markets. He's written an entire technical book about this whole notion of Radical Markets. So the research group spans from that to human computer interaction to

artificial intelligence. And we have GitHub, we have LinkedIn, we have a search advertising in news business, and probably a bunch of stuff that I'm embarrassingly not recounting in this. Like gaming two Xbox and so on. Yeah, gaming for sure. Like I was I was having a super fun conversation this morning with Phil Spencer. So when I was in college, there was this game that Lucas Arts made called Day of the Tentacle that my friends and I played forever. And like we're doing some interesting collaboration now with the folks who made Day of the Tentacle. And I was like completely nerding out with Tim Schaefer, like the guy who wrote Day of the Tentacle

this morning, just a complete fanboy, which, you know, sort of, it like happens a lot. Like, you know, Microsoft has been doing so much stuff. It's such breadth for such a long period of time that, you know, like being in CTO, like most of the time my job is very, very serious and sometimes that like I get to, I get caught up in like how amazing it is to be able to have the conversations that I have with the people I get to have them with. Yeah, to reach back into the sentimental and what's the radical markets in the economics? So the idea with radical markets is, can you come up with new market based mechanisms to, you know, I think we have this, we're having this debate right now, like, does capitalism

work? Like free markets work? Can the incentive structures that are built into these systems produce outcomes that are creating sort of equitably distributed benefits for every member of society. And I think it's a reasonable set of questions to be asking. And so one motor thought there, if you have doubts that the markets are actually working, you can sort can tip towards, like, okay, let's become more socialist and have central planning and governments or some other central organization is making a bunch of decisions about how work gets done and where the investments and where the outputs of those investments get distributed.

Glenn's notion is like lean more into like the market-based mechanism. So for instance, this is one of the more radical ideas. Suppose that you had a radical pricing mechanism for assets like real estate where you were, you could be bid out of your position in, in your home, uh, you know, for instance. So like if somebody came along and said, you know, like I've, I can find higher economic utility for this piece of real estate that you're running your, your business in, then you either have to bid to sort of stay or the thing that's got the higher economic utility

sort of takes over the asset, which would make it very difficult to have the same sort of rent-seeking behaviors that you've got right now because like if you did speculative bidding, like you would, you'd very quickly like lose a whole lot of money. And so like the prices of the assets would be sort of like very closely indexed to sort of like very closely index to like the value that they can produce. And like because like you'd have this sort of real time mechanism that would foreshute to sort of mark the value of the asset to the market, then it can be tax appropriately. Like you couldn't sort of sit on this thing

and say, oh, like this house is only worth 10,000 bucks when like everything around it is worth 10 million. That's finished. So it's an incentive structure that where the prices match the value, much better. Yeah. England does a much better job than I do in selling it. And I probably picked the world's worst example, you know, and in and. But like, and it's, it's intentionally provocative, you know, so like this whole notion,

like I, you know, like I'm not sure whether I like this notion that we can have a set of market mechanisms where I could get bit out of my property. But if you're thinking about something like Elizabeth Warren's wealth tax, for instance, you would be really interesting in how you would actually set the price on the assets and like you might have to have a mechanism like that if you put a tax like that in place. It's really interesting that that kind of research, at least tangentially is touching Microsoft research.

Yeah. You're really thinking broadly, maybe you can speak to, this connects to AI. So we have a candidate, Andrew Yang, who kind of talks about artificial intelligence and the concern that people have about automation's impact on society. And arguably, Microsoft is at the cutting edge of innovation in all these kinds of ways. And so it's pushing AI forward. How do you think about, combining all our conversations together here with radical markets and socialism

and innovation and AI that Microsoft is doing and then Andrew Yang's worry that that will result in job loss for the lower and so on. How do you think about that? I think it's sort of one of the most important questions and technology, like maybe even in society right now about how is AI going to develop over the course of the next several decades and what's it gonna be used for

and what benefits will it produce and what negative benefits will it produce and what negative impacts will it produce and how who gets to steer this whole thing. I'll say it at the highest level, one of the real joys of getting to do what I do at Microsoft is Microsoft has this heritage as a platform company. Bill has the sting that he said a bunch of years ago where the measure of a successful platform is that it produces far more economic value

for the people who build on top of the platform than is created for the platform owner or builder. I think we have to think about AI that way. Like, have it. Yeah, it has to be a platform that other people can use to build businesses, to fulfill their creative objectives, to be entrepreneurs, to solve problems that they have in their work and in their lives.

It can't be a thing where there are a handful of companies sitting in a very small handful of cities geographically who are making all the decisions about what goes into the AI and then on top of all this infrastructure, then build all of the commercially valuable uses for it. So I think that's bad from economics and equitable distribution of value perspective, like back to this whole notion of, like did the markets work. But I think it's also bad from an innovation perspective

because I have infinite amounts of faith in human beings that if you give folks powerful tools, they will go do interesting things. And it's more than just a few tens of thousands of people with the interesting tools. It should be millions of people with the tools. So it's sort of like, you know, you think about the steam engine in the late 18th century, like it was, you know, maybe the first large scale substitute for human labor that we've built, like a machine. And, you know, in the beginning, when these things are getting deployed, the folks who got most of the value from the steam engines were the folks who got most of the value from the seam engines were the folks who had capital,

so they could afford to build them, and like they built factories around them in businesses, and the experts who knew how to build and maintain them. But access to that technology democratized over time. Like now, like an engine is not a, it's not like a differentiated thing. Like there isn't one engine company that builds all the engines

and all of the things that use engines are made by this company and like they get all the economics from all of that. Like no, like fully demarcated, like they're probably, you know, we're sitting here in this room and like even though they don't, they're probably things, you know, like the, the MIMS gyroscope that are both of our, like,

there's like little engines, sort of everywhere. They're just a component in how we build the modern world, like AI needs to get there. Yeah, so that's a really powerful way to think. If we think of AI as a platform versus a tool that Microsoft owns as a platform that enables creation on top of it. That's the way to democratize it. That's really interesting, actually. Microsoft's history has been positioned well to do that.

And the tie back to this radical markets thing, so my team has been working with Glenn on this and Jaren linear actually. So Jaren is the father of virtual reality. He's one of the most interesting human beings on the planet, like a sweet, sweet guy. And so Jaren and Glenn and folks in my team have been working on this notion of data as labor or like they call it data dignity as well. And so the idea is that if you, you know, gang going back to this, you know, sort of industrial analogy,

if you think about data as the raw material that is consumed by the machine of AI in order to do useful things. Then we're not doing a really great job right now in having transparent marketplaces for valuing those data contributions. And we all make them explicitly. Like you go to LinkedIn, you sort of set up your profile on LinkedIn. That's an explicit contribution.

Like you know exactly the information that you're putting into the system. And like you put it there because you have some nominal notion of like what value you're gonna get in return. But it's like only nominal. Like you don't know exactly what value you're getting in return. Like services free, you know, like it's low amount of like perceived out. And then you've got all this indirect contribution

that you're making just by virtue of interacting with all of the technology that's in your daily life. And so like what Glenn and Jaren and this data dignity team are trying to do is like, can we figure out a set of mechanisms that let us value those data contributions so that you could create an economy and like a set of controls and incentives that would allow people to like maybe even in the limit like earn part of their

living through the data that they're creating. And like you can sort of see it in explicit ways, there are these companies like Scale AI and like they are a whole bunch of them in China right now that are basically data labeling companies. So like you're doing supervised machine learning, you need lots and lots of label training data. And like those people are getting like who work for those companies are getting compensated for their data contributions into the system. And so that's easier to put a number on their contribution because they're explicitly labeling data. Correct.

But you're saying that we're all contributing data in different kinds of ways. And it's fascinating to start to explicitly try to put a number on it. Do you think that's possible? I don't know. It's hard. It really is.

Because we don't have as much transparency as I think we need in how the data is getting used. It's super complicated. I think it's technologists appreciate some of the subtlety there. The's like, you know, the data, the data gets created. And then it gets, you know, it's not valuable. Like the data exhaust that you give off or the, you know, the explicit data that I am putting into the system isn't valuable, valuable, isn't super valuable, atomically. Like, it's only valuable when you aggregate it together and to large numbers.

This is true even for these folks who are getting compensated for labeling things, for a supervised machine learning now, you need lots of labels to train a model that performs well. And so I think that's one of the challenges. It's like, how do you figure out because this data's getting combined in so many ways

through these combinations, like how the value is flowing? Yeah, that's fast enough. It's fascinating that you're thinking about this. I wasn't even going into this conversation expecting the breadth of research, really, that Microsoft Broadleaf is thinking about. You are thinking about it, Microsoft. So if we go back to 89, when Microsoft released office or 1990, when they released Windows 3.0, how's the, in your

view, I know you weren't there the entire, you know, through his history, but how has the company changed in the 30 years since as you look at it now? The good thing is it started off as a platform company. Like, it's still a platform company, like the parts of the business that are like thriving and most successful or those that are building platforms. Like the mission of the company now is, the mission's changed. It's like changing a very interesting way.

So, you know, back in 89.9, like they were still on the original mission, which was like put a PC on every desk and in every home. Like, and it was basically about democratizing access to this new personal computing technology, which when Bill started the company, integrated circuit microprocessors were a brand new thing. And people were building home-brew computers from kits like the way people build ham radios right now And I think this is sort of the interesting thing for folks who build platforms in general

Bill saw The opportunity there and what personal computers could do and it was like it was sort of a reach like you just sort of imagine like where things were You know when they started the company versus where things are now like In success when you've democratized a platform, it just sort of vanishes into the platform, you don't pay attention to it anymore. Operating systems aren't a thing anymore. They're super important, completely critical. When you see one fail, you just sort of understand.

But it's not a thing where you're not waiting for the next operating system thing in the same way that you were in 1995, right? That's right. Like in 1995, like, you know, we had rolling stones on the stage with the Windows 95 roll out. Like it was like the biggest thing in the world. Everybody was lined up for it in the way that people used to line up for iPhone. But like, you know, eventually, and like, this isn't necessarily a bad thing. Like it just sort of, you know, the success is that it sort of, it becomes ubiquitous. It's like everywhere and like human beings when their technology becomes ubiquitous,

they just sort of start taking it for granted. So the mission now that Satya rearticulated five plus years ago now when he took over his CEO the company Our mission is to Empower every individual and every organization in the world to be more successful and So you know again like that's a platform Mission and like the way that we do it now is is different. It's like we have a hyperscale cloud that people are building their applications on top of. Like we have a bunch of AI infrastructure that people are building their AI applications on top of.

We have, you know, we have a productivity suite of software like Microsoft Dynamics, which, you know, some people might not think is the sexiest thing in the world, but it's like helping people figure out how to automate all of their business processes and workflows and to help those businesses using it to grow and be more. So it's a much broader vision in a way now than it was back then. It was sort of very particular thing. And now we live in this world

where technology is so powerful that it's like such a basic fact of life that it, you know, that it both exists and is going to get better and better over time, or at least more and more powerful over time. So like, you know, what you have to do as a platform player is just much bigger. Right.

There's so many directions in which you can transform. You didn't mention mixed reality, too. You know, that's, that's probably early days, or depends how you think of it. But if we think in a scale of centuries, it's the early days of mixed reality. Oh, for sure. And so, you know, with how it lends the Microsoft is doing some really

interesting work there. Do you touch that part of the effort? What's the thinking? Do you think of mixed reality as a platform to? Oh, sure. When we look at what the platforms of the future could be, so like fairly obvious that like AI is one, like you don't have to, I mean, like that's, you know, you sort of say it to, like, someone and, you know, like they get it. But like we also think of the, like, mixed reality and quantum is, like, these two interesting, you know, potentially computing. Yeah. Okay, so let's get crazy then. So you're talking about some futuristic things here. Well, the mixed reality Microsoft is really not even futuristic is here.

It is incredible stuff. And look, and it's having an impact right now. Like one of the more interesting things that's happened with mixed reality over the past a couple of years that I didn't clearly see is that it's become the computing device for folks who, for doing their work who haven't used any computing device at all to do their work before. So technicians and service folks and people who are doing like machine maintenance on factory floors. So like they, you know, because they're mobile and like they're out in the world

and they're working with their hands and sort of servicing these like very complicated things, they don't use their mobile phone and like they don't carry a laptop with them and they're not tethered to a desk. And so mixed reality, like where it's getting traction right now where HoloLid's is selling a lot of a lot of units is for the source of applications for these workers and it's become like, I mean, like the

people love it. They're like, oh my god, like this is like for them, like the same sort of productivity boost that, you know, like an office worker had when they got their first personal computer. that an office worker had when they got their first personal computer. Yeah, but you did mention it's certainly obvious AI as a platform, but can we dig into it a little bit? Sure. How does AI begin to infuse some of the products in Microsoft? So currently providing training of, for example, neural networks in the cloud, or providing pre-trained models, or just even providing computing resources, whatever different inference that you want to do using neural networks.

Yeah. How do you think of AI infusing the, as a platform that Microsoft can provide? Yeah. I mean, I think it's super interesting. It's like everywhere. And we run these review meetings now where it's the Insatiya and members of Satiya's leadership team and like a cross-functional group of folks across the entire company who are working on, like

either AI infrastructure or like have some substantial part of their product work using AI in some significant way. Now, the important thing to understand is like when you think about like how the AI is going to manifest in like an experience how the AI is going to manifest in an experience for something that's going to make it better, I think you don't want the AIness to be the first order thing. It's like whatever the product is and the thing that is trying to help you do, the AI just sort of makes it better.

This is a gross exaggeration, but people get super excited about where the AI just sort of makes it better. And this is a gross exaggeration, but like I, people get super excited about like where the AI is showing up in products and I'm like, do you get that excited about like where you're using a hash table, like in your code? Like it's just another. It's a very interesting programming tool,

but it's sort of like it's an engineering tool. And so like it shows up everywhere. So, we've got dozens and dozens of features now in office that are powered by fairly sophisticated machine learning. Our search engine wouldn't work at all if you took the machine learning out of it, the increasingly, you know, things like content moderation on our Xbox and X Cloud platform. Yeah. When you mean moderation, you mean like the recommended is like showing what you want to look at next.

No, no, it's like anti-bullying stuff. So the usual social network stuff that you have to deal with. Yeah, correct. But it's like really it's targeted towards a gaming audience. So it's like a very particular type of thing where you know, the line between playful banter and like legitimate bullying is like a subtle one and like you have to like it's sort of tough. Like I have, I have. I'd love to, if we could dig into it, because you're also, you were led to engineering efforts at LinkedIn. Yep.

And if we look at, if we look at LinkedIn as a social network, and if we look at the Xbox gaming, as the social components, the very different kinds of, I imagine, communication going on on the two platforms, right, and the line in terms of bullying and so on is different on the platforms. So, how do you, I mean, such a fascinating philosophical discussion of where that line is. I don't think anyone knows the right answer. Twitter folks are under fire now.

The Jack, a Twitter for trying to find that line. Nobody knows what that line is, but how do you try to find the line that for, you know, trying to prevent abusive behavior and at the same time let people be playful and joke around and that kind of thing? I think in a certain way, like, you know, if you have what I would call vertical social networks, it gets to be a little bit easier. So like if you have a clear notion of like what your social network should be used for or like what you are designing a community around, then you don't have as many dimensions to your sort of content safety problem as you do

in a general purpose platform. I mean, so like on LinkedIn, like the whole social network is about connecting people with opportunity, whether it's helping them find a job or to find mentors or to help them find their next sales lead or to just allow them to broadcast their professional identity to their network of peers and collaborators and professional community. That is, in some ways, that's very, very broad, but in other ways, it's narrow. You can build AI's machine learning systems that are capable with those boundaries of making better automated decisions about what is is you know, sort of inappropriate and

offensive comment or dangerous comment or illegal content. When you have some constraints, you know, same thing with Yeah, same thing with like the gaming gaming social networks, except for instance like it's about playing games about having fun And like the thing that you don't want to have happen on the platform is why bullying is such an important thing. Like bullying is not fun. So you want to do everything in your power to encourage that not to happen. And yeah, but I think it's sort of a tough problem in general, is one where I think, you know, eventually we're going to have to have tough problem in general is one where I think eventually we're going to have to have some sort of clarification from our policy makers about what it is that we should be doing,

like where the lines are because it's tough. Like you don't, like in democracy, right? Like you don't want, you want some sort of democratic involvement, like people should have a say in like where, where the lines, lines are drawn. Like you don't want a bunch of people making like unilateral decisions. And like we are in a, we're in a state right now for some of these platforms where you actually do have to make unilateral decisions where the policy making isn't going to happen fast enough in order to prevent very bad things from happening.

But we need the policy making side of that to catch up, I think, as quickly as possible, because you want that whole process to be a democratic thing, not a weird thing where you've got a non-representative group of people making decisions that have, you know, like national and global impact. It's fascinating because the digital space is different than the physical space in which nations and governments were established. And so what policy looks like globally, what bullying looks like globally, what's healthy communication looks like, global is open question. And we're all figuring it out together. Yeah, I mean, with fake news, for instance, and fake news generated by humans.

Yeah, so we can talk about deepfakes. I think that is another very interesting level of complexity. But like, if you think about just the written word, right? Like, we have, you know, we invented papyrus what's 3,000 years ago where you could sort of put word on paper. And then, 500 years ago, like we get the printing press, like where the word gets a little bit more ubiquitous.

Then you really didn't get ubiquitous, print it word until the end of the 19th century when the offset press was invented. Then just explodes, the cross product of that and the industrial revolutions need for educated citizens resulted in like this rapid expansion of literacy and the rapid expansion of the word. But like we had 3000 years up to that point to figure out like how to, you know,

like what's journalism, what's editorial integrity, like what's, you know like what's scientific peer review. And so like you built all of this mechanism to like try to filter through all of the noise that the technology made possible to like sort of getting to something that society could cope with. And like if you think about just the piece,

the PC didn't exist 50 years ago. And so in like this span of, you know, like half a century, like we've gone from no digital, you know, no ubiquitous digital technology to like having a device that sits in your pocket where you can sort of say whatever is on your mind to like, what would it marry have? And our Mary Meeker just released her new slide deck last week. You know, we've got 50% penetration of the internet to the global population. Like there's like three and a half billion people who are connected now. So it's like, it's crazy. Crazy.

Like inconceivable. Like, how fast all of this happens. So, you know, it's not surprising that we haven't figured out what to do yet, but like we got to, like we got to really, like lean into this set of problems because like we basically have three millennia worth of work to do about how to deal with all of this and like probably what, you know, amounts to the next decade worth of time. So, since one, the topic of tough challenging problems, let's look at more on the tooling side in AI that Microsoft is looking at face recognition software. So there's a lot of powerful positive use cases for face recognition, but there's some

negative ones that we've seen those in different governments in the world. So, how do you, how does Microsoft think about the use of face recognition, software, as a platform in governments and companies? How do we strike an ethical balance here? Yeah, I think we've articulated a clear point of view. So Brad Smith wrote a blog post last fall, I believe, that's sort of like outline very specifically what our point of view is there. And I think we believe that there are certain uses to which face recognition should not be put.

And we believe again that there's a need for regulation there. Like the government should really come in and say that, this is where the lines are. And we very much wanted to figuring out where the lines are should be a democratic process. But in the short term, we've drawn some lines where we push back against uses of face recognition technology. Like the city of San Francisco, for instance,

I think is completely outlawed any government agency from using face recognition tech. And that may prove to be a little bit overly broad. But for certain law enforcement things, I would personally rather be overly cautious in terms of restricting use of it until we have defined a reasonable democratically determined regulatory framework for where we could and should use it. The other thing there is we've got a bunch of research that we're doing in a bunch of progress that we've made on bias there.

There are all sorts of weird biases that these models can have all the way from the most noteworthy one where you may have underrepresented minorities who are underrepresented in the training data and then you start learning strange things. But they're even other weird things. I think we've seen in the public research, like models can learn strange things, like all doctors or men, for instance. Yeah, I mean, so like, it really is a thing where it's very important for everybody who is working on these things before they push publish. everybody who is working on these things before they push publish, they launch the experiment, they push the code to online, or they even publish the paper that they are at least starting to think about what some of the potential negative consequences are or some of this stuff. I mean, this is where, you know, like the deep fake stuff, I find very worrisome just because

they're going to be some very good beneficial uses of like Gan generated imagery. And like, and funny enough, like one of the places where it's actually useful is we're using the technology right now to generate synthetic, synthetic visual data for training some of the face recognition models to get rid of the bias. So like that's one like super good use of the tech, but like You know, it's getting good enough now where you know, it's gonna sort of challenge a normal human being's ability to like now You're just sort of say like it's it's very expensive for someone to

Fabricate a photorealistic fake video and like Gans are gonna make it fantastically cheap to fabricate a photorealistic fake video. And so like what you assume you can sort of trust as true versus like be skeptical about is about to change. And like we're not ready for it I don't think. The nature of truth, right? That's it's also exciting because I think both you and I probably would agree that the way to solve to take on that challenge is technology. Yeah, right. There's probably going to be ideas of ways to verify which kind of video is legitimate, which kind is not. So to me, that's an exciting possibility, most likely for just the comedic genius that the internet usually creates with these kinds of videos. And hopefully will not result in any serious harm.

Yeah, and it could be, you know, like I think we will have technology to that may be able to detect whether or not something's fake or real, although the fakes are pretty convincing even when you subject them to machine scrutiny. But we also have these increasingly interesting social networks that are under fire right now for some of the bad things that they do. Like one of the things you could choose to do with a social network is like you could use crypto and the networks to like have content signed where you could have a like full chain of custody that accompanied every piece of content. So like when you're viewing something

and like you wanna ask yourself like how, you know, how much can I trust this like when you're viewing something and like you want to ask yourself like how you know how much can I trust this like you can click something and like have a verified chain of custody that shows like oh this is coming from you know from this source and it's like signed by like someone who's identity I trust. Yeah. Yeah I think having that you know having that chain of custody like being able to like say, here's this video, like, it may or may not have been produced using some of this deep fake technology. But if you've got a verified chain of custody where you can sort of trace it all the way back to an identity and you can decide whether or not, like, I trust this identity, like, Oh, no, this is really from the White House, or like, this is really from the, you know,

the office of this particular presidential candidate or it's really from Jeff Weiner CEO of LinkedIn or Satya Nadella CEO of Microsoft. That might be one way that you can solve some of the problems. That's not the super high tech. We've had all of this technology forever. But I think you're right. It has to be some sort of technological thing because the underlying tech that is used to create this is not going to do anything but get better over time and the genie is out of the bottle. There's no stuffing it back in. And there's a social component which I think is really healthy for democracy, where people will be skeptical about the thing they watch. Yeah.

In general, which is good. Skepticism in general is good for the content. And it's good. So deep-akes, in that sense, are creating global skepticism about can they trust what they read? It encourages further research. I come from the Soviet Union, where basically nobody trusted the media because you knew it was propaganda,

and that kind of skepticism encouraged further research about ideas, supposed to just trusting any one source. Well, I think it's one of the reasons why the scientific method and our apparatus of modern science is so good, because you don't have to trust anything. The whole notion of modern science beyond the fact that this is a hypothesis, and this is an experiment to test the hypothesis, and this is, like this is a peer review process for scrutinizing published results.

But like, stuff's also supposed to be reproducible. So like, you know, it's been vetted by this process, but like you also are expected to publish enough detail where, you know, if you are sufficiently skeptical of the thing, you can go try to like reproduce it yourself. And like, I don't know what it is. Like, I think a lot of engineers are like this where this brain is wired for skepticism.

Like you don't just first order trust everything that you see in encounter and you're curious to understand the next thing. But I think it's an entirely healthy thing. thing. And like we need a little bit more of that right now. So I'm not a large business owner. So I'm just, I'm just a huge fan of many of Microsoft products. I mean, I still, actually in terms of I generate a lot of graphics and images, and I still use PowerPoint to do that. Pete's illustrator for me, I still, actually in terms of I generate a lot of graphics and images and I still use PowerPoint to do that. It beats Illustrator for me, even professional sort of, it's fascinating.

So I wonder what is the future of, let's say, Windows and Office look like? Do you see it? I mean, I remember looking forward to XP. Was an exciting, when XP was released, just like you said. I don't remember when 95 was released, but XP for me was a big celebration. And when Ten came out, I was like, okay, well, it's nice, it's a nice improvement. But so what do you see the future of these products?

Yeah, I think there's a bunch of exciting. I mean, on the office front, there's gonna be this like increasing productivity wins that are coming out of some of these AI powered features that are coming, like the products are sort of get smarter and smarter and like a very subtle way. Like there's not gonna be this big bang moment

where, you know, like Clippy is gonna re-emerge and it's gonna be. Wait a minute. Okay, we'll have to wait, wait, wait, like, Clippy is gonna re-emerge and it's gonna be- Wait a minute. Okay, we'll have to wait, wait, wait, wait. It's Clippy coming back. Well, it's quite seriously. So, injection of AI, there's not much, or at least I'm not familiar, sort of assistive type of stuff going on inside the office products, like a Clippy-style, assistant, personal assistant. Do you think that's, there's a possibility of that in the future? Yeah, I think there are a bunch of like very small ways in which like machine learning

power to assistive things are in the product right now. So there are, there are a bunch of interesting things like the auto response stuff's getting better and better and it's like getting to the point where you know it can auto respond with like okay, like you know this person's clearly trying to schedule a meeting so it looks at your calendar and it automatically like tries to find like a time and a space that's mutually interesting like a time and a space that's mutually interesting. Like we have this notion of Microsoft search where it's like not just web search, but it's like search across all of your information

that's sitting inside of like your Office 365 tenant and potentially in other products. And we have this thing called the Microsoft Graph that is basically a API faderator that sort of like gets you hooked up across the entire breadth of all of the what were information silos before they got woven together with the graph. Like that is like getting increasing,

with increasing effectiveness sort of plumbed into some of these audit response things where you're gonna be able to see the system like automatically retrieve information for you. Like if, you know, like I frequently send out, you know, emails to folks where like I can't find a paper or a document or what not. There's no reason why the system won't be able

to do that for you. And like, I think the, the, it's building towards like having things that look more like, like a fully integrated, you know, assistant. But like you, you'll have a bunch of steps that you will see before you, like it will not be this like big bang thing where like Clippy comes back and you've got this, like, you know, manifestation of, you got this manifestation of a fully powered assistant. So I think that's definitely coming in like all of the collaboration co-authoring stuff

getting better. It's really interesting. If you look at how we use the office product portfolio at Microsoft, more and more of it is happening inside of teams as a canvas. And it's this thing where you've got collaboration is at the center of the product. And we built some really cool stuff that's some of which is about to be open source that are sort of Framework level things for doing for doing co-authoring So in is there a cloud component to that? So on the web or is it

If you give me if I don't already know this but with Office 365 We still the collaboration we do if we're doing word, we're still sending the file around. No, we're already a little bit better than that. And like, you know, so like the fact that you're on a wearer means we've got a better job to do, like helping you discover, discover this stuff. But yeah, I mean, it's already like got a huge, huge cloud component. And like part of, you know, part of this framework stuff,

I think we're calling it like we've been working on it for a couple of years. So like I know the internal code name for it, but I think when we launch it a bill, it's called the fluid framework. And but like what fluid lets you do is like you can go into a conversation that you're having in teams and like reference like part of a spreadsheet that You're working on where somebody's like sitting in the Excel canvas like working on the spreadsheet with a you know charter whatnot

And like you can sort of embed like part of the spreadsheet and the team's conversation where like you can Dynamically updated and like all of the Changes that you're making to this object or like coordinate and everything is sort of updating in real time. So you can be in whatever canvas is most convenient for you to get your work done. So out of my own sort of curiosity is engineer.

I know what it's like to sort of lead a team of 10, 15 engineers. Microsoft has, I don't know what it's like to sort of lead a team of 1015 engineers. Microsoft has, I don't know what the numbers are, maybe 15, maybe 60,000 engineers. A lot of engineers. I don't know exactly what the number is, it's a lot. It's tens of thousands. Right, this is more than 10 or 15. What, I mean, you've, you've led different sizes, mostly large size of engineers.

What does it take to lead such a large group, to continue innovation, continue being highly productive and yet develop all kinds of new ideas and yet maintain, like, what does it take to lead such a large group of brilliant people? I think the thing that you learn as you manage larger and larger scale is that there are three things that are very, very important for big engineering teams. One is having some sort of forethought about what it is that you're gonna be building over large periods of time. Like not exactly, like you don't need to know

that like you know I'm putting all my chips on this one product and like this is gonna be the thing, but like it's useful to know like what sort of capabilities you think you're going to need to have to build the products of the future. And then like invest in that infrastructure, like whether, and I'm not just talking about storage systems or cloud APIs, it's also like,

what does your development process look like? What tools do you want? Like, what culture do you want to build around? Like how you're sort of collaborating together to like make complicated technical things. And so like having an opinion and investing in that is like it just gets more and more important. And like the sooner you can get a concrete set of opinions,

like the better you're going to be. Like you can wing it for a while, small scales. Like you know, when you start a company, like you don't have to be like super specific about it. But like the biggest miseries that I've ever seen as an engineering leader are in places where you didn't have a clear enough opinion about those things soon enough.

And then you just sort of go create a bunch of technical debt and like culture debt that is excruciatingly painful to clean up. So like that's one bundle of things. Like the other bundle of things is, like it's just really, really important to have a clear mission that's not just some cute crap you say because you think you should have a mission

But like something that clarifies for people like where it is that you're headed together Like I know it's like probably like a little bit too popular right now, but You've all Ferrari book sapiens one of the central ideas in his book is that story telling is the quintessential thing for coordinating the activities of large groups of people. Once you get past Dunbar's number, and I've really, really seen that just managing engineering teams like you can, you can just brute force things when you're less than 120, 150 folks where you can sort of know and

trust and understand what the dynamics are between all the people. But like past that, like things just sort of start to catastrophically fail if you don't have some sort of set of share goals that you're marching towards. And so even though it sounds touchy-feely, and a bunch of technical people will sort of bulk at the idea that you need to have a clear, like the missions,

like very, very, very important. You have all right, right? Stories, that's how our society, that's the fabric that connects us all of us is these powerful stories and that that works for companies too right. It works for everything like I mean even down to like you know you sort of really think about like our currency for instance is a story. A constitution is a story our laws are still I mean like we believe very very very strongly in them.

And thank God we do. But like they are, they're just abstract things. Like they're just words. Like we don't believe in them. They're nothing. And in some sense, those stories are platforms and the kinds, some of which Microsoft is creating, right? They have platforms on which we define the future.

So last question, what do you, let's give philosophical maybe, bigger than even Microsoft, what do you think the next 20, 30 plus years looks like for computing, for technology, for devices? Do you have crazy ideas about the future of the world? Yeah, look, I think we, you know, we're entering this time where we've got, we have technology that is progressing at the fastest rate that it ever has, and you've got, you get some really big social problems, like society scale problems that we have to tackle. And so, you know, I think we're going to rise to the challenge

and figure out how to intersect all of the power of this technology with all of the big challenges that are facing us, whether it's global warming, whether it's like the biggest remainder of the population boom is in Africa for the next 50 years or so. And global warming is going to make it increasingly difficult to feed global population in particular like in this place where you're going to have like the biggest population boom. I think we you know like AI is going to like if we push it in the right direction like

you can do like incredible things to empower all of us to achieve our full potential and to, you know, like live better lives, but like that also means focus on like some super important things, like how can you apply it to healthcare to make sure that, you know, like our quality and cost of, and sort of ubiquity of health coverage is better and better over time. Like that's more and more important every day is like in the United States and like the rest of the industrialized world. So Western Europe, China, Japan, Korea, like you've got this population bubble of aging, working age folks who are, you know, at some point over the next 20, 30 years,

they're gonna be largely retired and like you're gonna have more retired people than working age people. And then like you've got, you know, sort of natural questions about who's gonna take care of all the old folks and who's gonna do all the work. And the answers to like care of all the old folks and who's going to do all the work and The answers to like all of these sorts of questions like where you're sort of running into, you know like constraints of the

You know the the the world and a society has always been like what tech is gonna like help us get around this You know like when I was when I was a kid in the 70s and 80s, like we talked all the time about like, oh, the like population boom, population boom, like we're going to, like we're not going to be able to like feed the planet. And like we were like right in the middle of the green revolution where like this, this massive technology driven increase and crop productivity, like worldwide. And like some of that was like taking some of the things that we knew in the West and like getting them distributed to the developing world. And like part of it were things like,

just smarter biology like helping us increase. And like we don't talk about like, yep, overpopulation anymore because like we can more or less, We sort of figured out how to feed the world like that's a that's a technology story Yeah, and so like I'm super super hopeful about the future and in the ways where We will be able to apply technology to solve some of these super challenging problems like I've I've apply technology to solve some of these super challenging problems.

Like one of the things that I'm trying to spend my time doing right now is trying to get everybody else to be hopeful as well, because back to Harari, we are the stories that we tell. Like if we get overly pessimistic right now about the potential future of technology. We may fail to get all the things in place that we need to have our best possible future. That hopeful optimism, I'm glad that you have it because you're leading large groups of engineers that are actually defining, that are writing that story, that are helping build that future, which is super exciting. And I agree with everything you said except I do hope Clippy comes back. We miss him. I speak for the people.

So, Gellin, thank you so much for talking to me. Oh, thank you so much for having me. It was pleasure.